ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2202.08975
  4. Cited By
Probing Pretrained Models of Source Code

Probing Pretrained Models of Source Code

16 February 2022
Sergey Troshin
Nadezhda Chirkova
    ELM
ArXivPDFHTML

Papers citing "Probing Pretrained Models of Source Code"

24 / 24 papers shown
Title
ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding
ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding
Indraneil Paul
Haoyi Yang
Goran Glavas
Kristian Kersting
Iryna Gurevych
AAML
SyDa
34
0
0
27 Mar 2025
Toward Neurosymbolic Program Comprehension
Toward Neurosymbolic Program Comprehension
Alejandro Velasco
Aya Garryyeva
David Nader-Palacio
Antonio Mastropaolo
Denys Poshyvanyk
29
0
0
03 Feb 2025
A Survey on Adversarial Machine Learning for Code Data: Realistic
  Threats, Countermeasures, and Interpretations
A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations
Yulong Yang
Haoran Fan
Chenhao Lin
Qian Li
Zhengyu Zhao
Chao Shen
Xiaohong Guan
AAML
28
0
0
12 Nov 2024
Meta-Models: An Architecture for Decoding LLM Behaviors Through
  Interpreted Embeddings and Natural Language
Meta-Models: An Architecture for Decoding LLM Behaviors Through Interpreted Embeddings and Natural Language
Anthony Costarelli
Mat Allen
Severin Field
11
1
0
03 Oct 2024
A Critical Study of What Code-LLMs (Do Not) Learn
A Critical Study of What Code-LLMs (Do Not) Learn
Abhinav Anand
Shweta Verma
Krishna Narasimhan
Mira Mezini
22
1
0
17 Jun 2024
On the Limitations of Embedding Based Methods for Measuring Functional
  Correctness for Code Generation
On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation
Atharva Naik
27
1
0
26 Apr 2024
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards
  Efficient Code Generation
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation
Zhensu Sun
Xiaoning Du
Zhou Yang
Li Li
David Lo
28
10
0
25 Apr 2024
Structure-aware Fine-tuning for Code Pre-trained Models
Structure-aware Fine-tuning for Code Pre-trained Models
Jiayi Wu
Renyu Zhu
Nuo Chen
Qiushi Sun
Xiang Li
Ming Gao
22
2
0
11 Apr 2024
Astraios: Parameter-Efficient Instruction Tuning Code Large Language
  Models
Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
Terry Yue Zhuo
A. Zebaze
Nitchakarn Suppattarachai
Leandro von Werra
H. D. Vries
Qian Liu
Niklas Muennighoff
ALM
23
8
0
01 Jan 2024
INSPECT: Intrinsic and Systematic Probing Evaluation for Code
  Transformers
INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers
Anjan Karmakar
Romain Robbes
14
2
0
08 Dec 2023
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API
  Names?
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?
Terry Yue Zhuo
Xiaoning Du
Zhenchang Xing
Jiamou Sun
Haowei Quan
Li Li
Liming Zhu
21
2
0
14 Sep 2023
Evaluating and Explaining Large Language Models for Code Using Syntactic
  Structures
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures
David Nader-Palacio
Alejandro Velasco
Daniel Rodríguez-Cárdenas
Kevin Moran
Denys Poshyvanyk
26
8
0
07 Aug 2023
Substance or Style: What Does Your Image Embedding Know?
Substance or Style: What Does Your Image Embedding Know?
Cyrus Rashtchian
Charles Herrmann
Chun-Sung Ferng
Ayan Chakrabarti
Dilip Krishnan
Deqing Sun
Da-Cheng Juan
Andrew Tomkins
10
6
0
10 Jul 2023
Exploring the Robustness of Large Language Models for Solving
  Programming Problems
Exploring the Robustness of Large Language Models for Solving Programming Problems
Atsushi Shirafuji
Yutaka Watanobe
Takumi Ito
Makoto Morishita
Yuki Nakamura
Yusuke Oda
Jun Suzuki
ELM
23
17
0
26 Jun 2023
Towards Understanding What Code Language Models Learned
Towards Understanding What Code Language Models Learned
Toufique Ahmed
Dian Yu
Chen Huang
Cathy Wang
Prem Devanbu
Kenji Sagae
ELM
30
5
0
20 Jun 2023
Towards Efficient Fine-tuning of Pre-trained Code Models: An
  Experimental Study and Beyond
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Ensheng Shi
Yanlin Wang
Hongyu Zhang
Lun Du
Shi Han
Dongmei Zhang
Hongbin Sun
12
41
0
11 Apr 2023
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics
  Capacities
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
Wei Ma
Shangqing Liu
Mengjie Zhao
Xiaofei Xie
Wenhan Wang
Q. Hu
Jiexin Zhang
Yang Liu
11
15
0
20 Dec 2022
SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in
  Fine-tuned Source Code Models
SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models
Hossein Hajipour
Ning Yu
Cristian-Alexandru Staicu
Mario Fritz
OODD
9
4
0
10 Oct 2022
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models
  for Programming Language Attend Code Structure
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
Nuo Chen
Qiushi Sun
Renyu Zhu
Xiang Li
Xuesong Lu
Ming Gao
20
10
0
07 Oct 2022
AST-Probe: Recovering abstract syntax trees from hidden representations
  of pre-trained language models
AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models
José Antonio Hernández López
M. Weyssow
Jesús Sánchez Cuadrado
H. Sahraoui
14
22
0
23 Jun 2022
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for
  Code Understanding and Generation
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
Yue Wang
Weishi Wang
Shafiq R. Joty
S. Hoi
196
1,451
0
02 Sep 2021
Probing Classifiers: Promises, Shortcomings, and Advances
Probing Classifiers: Promises, Shortcomings, and Advances
Yonatan Belinkov
221
291
0
24 Feb 2021
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding
  and Generation
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu
Daya Guo
Shuo Ren
Junjie Huang
Alexey Svyatkovskiy
...
Nan Duan
Neel Sundaresan
Shao Kun Deng
Shengyu Fu
Shujie Liu
ELM
183
1,098
0
09 Feb 2021
What you can cram into a single vector: Probing sentence embeddings for
  linguistic properties
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau
Germán Kruszewski
Guillaume Lample
Loïc Barrault
Marco Baroni
196
876
0
03 May 2018
1